3D representations have also been studied since the early years of gait recognition.
For example, Urtasun and Fua [1]} proposed an approach to gait analysis that depended on 3D temporal motion models using an articulated skeleton.
Zhao et al. [2]} applied a local optimization algorithm to track 3D motion for gait recognition.
Yamauchi et al. [3]} proposed the first method using 3D pose estimated from RGB frames for walking human recognition.
Ariyanto and Nixon [4]} built a 3D voxel-based dataset using a complex multi-camera system and proposed a structural model of articulated cylinders with 3D Degrees of Freedom at each joint to model the human lower legs.
However, these methods either discard rich 3D information like viewpoints and shapes or are limited by devices for real-world applications.
In summary, to overcome the problem of 2D methods and explore 3D representations for gait recognition in the wild, we aim to explore 3D mesh as a rich representation of the viewpoint and shape of the human body.
